

'''simple docstring'''





import os
import sys
import warnings
from dataclasses import dataclass, field
from io import BytesIO
from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union

import numpy as np
import pyarrow as pa

from .. import config
from ..download.streaming_download_manager import xopen
from ..table import array_cast
from ..utils.file_utils import is_local_path
from ..utils.py_utils import first_non_null_value, no_op_if_value_is_null, string_to_dict


if TYPE_CHECKING:
							import PIL.Image

							from .features import FeatureType


UpperCamelCase_    						=       None
UpperCamelCase_    						=       """<""" if sys.byteorder == """little""" else """>"""
# Origin: https://github.com/python-pillow/Pillow/blob/698951e19e19972aeed56df686868f1329981c12/src/PIL/Image.py#L3126 minus "|i1" which values are not preserved correctly when saving and loading an image
UpperCamelCase_    						=       [
    np.dtype("""|b1"""),
    np.dtype("""|u1"""),
    np.dtype("""<u2"""),
    np.dtype(""">u2"""),
    np.dtype("""<i2"""),
    np.dtype(""">i2"""),
    np.dtype("""<u4"""),
    np.dtype(""">u4"""),
    np.dtype("""<i4"""),
    np.dtype(""">i4"""),
    np.dtype("""<f4"""),
    np.dtype(""">f4"""),
    np.dtype("""<f8"""),
    np.dtype(""">f8"""),
]





@dataclass
class 		__SCREAMING_SNAKE_CASE							:
				lowerCamelCase_		       =    True
				lowerCamelCase_		       =    None
				# Automatically constructed
				lowerCamelCase_		       =    "PIL.Image.Image"
				lowerCamelCase_		       =    pa.struct({'bytes': pa.binary(), 'path': pa.string()}      )
				lowerCamelCase_		       =    field(default='Image'						,							init=lowercase__						,							repr=lowercase__      )







				def __call__(       self     :		str   ):
										'''simple docstring'''
										return self.pa_type







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		Union[str, bytes, dict, np.ndarray, "PIL.Image.Image"]   ):
										'''simple docstring'''
										if config.PIL_AVAILABLE:
																import PIL.Image
										else:
																raise ImportError('''To support encoding images, please install \'Pillow\'.'''   )

										if isinstance(UpperCAmelCase__		,					UpperCAmelCase__   ):
																lowercase :							Tuple    		=np.array(UpperCAmelCase__   )

										if isinstance(UpperCAmelCase__		,					UpperCAmelCase__   ):
																return {"path": value, "bytes": None}
										elif isinstance(UpperCAmelCase__		,					UpperCAmelCase__   ):
																return {"path": None, "bytes": value}
										elif isinstance(UpperCAmelCase__		,					np.ndarray   ):
																# convert the image array to PNG/TIFF bytes
																return encode_np_array(UpperCAmelCase__   )
										elif isinstance(UpperCAmelCase__		,					PIL.Image.Image   ):
																# convert the PIL image to bytes (default format is PNG/TIFF)
																return encode_pil_image(UpperCAmelCase__   )
										elif value.get('''path'''   ) is not None and os.path.isfile(value['''path''']   ):
																# we set "bytes": None to not duplicate the data if they're already available locally
																return {"bytes": None, "path": value.get('''path'''   )}
										elif value.get('''bytes'''   ) is not None or value.get('''path'''   ) is not None:
																# store the image bytes, and path is used to infer the image format using the file extension
																return {"bytes": value.get('''bytes'''   ), "path": value.get('''path'''   )}
										else:
																raise ValueError(
																    F'''An image sample should have one of \'path\' or \'bytes\' but they are missing or None in {value}.'''   )







				def  lowerCamelCase_	(       self     :		List[str]		,					UpperCAmelCase__     :		dict		,					UpperCAmelCase__     :		Optional[int]=None   ):
										'''simple docstring'''
										if not self.decode:
																raise RuntimeError('''Decoding is disabled for this feature. Please use Image(decode=True) instead.'''   )

										if config.PIL_AVAILABLE:
																import PIL.Image
										else:
																raise ImportError('''To support decoding images, please install \'Pillow\'.'''   )

										if token_per_repo_id is None:
																lowercase :							Tuple    		={}

										lowercase		,	lowercase :							List[Any]    		=value['''path'''], value['''bytes''']
										if bytes_ is None:
																if path is None:
																						raise ValueError(F'''An image should have one of \'path\' or \'bytes\' but both are None in {value}.'''   )
																else:
																						if is_local_path(UpperCAmelCase__   ):
																												lowercase :							int    		=PIL.Image.open(UpperCAmelCase__   )
																						else:
																												lowercase :							Optional[Any]    		=path.split('''::'''   )[-1]
																												try:
																																		lowercase :							Any    		=string_to_dict(UpperCAmelCase__		,					config.HUB_DATASETS_URL   )['''repo_id''']
																																		lowercase :							Union[str, Any]    		=token_per_repo_id.get(UpperCAmelCase__   )
																												except ValueError:
																																		lowercase :							Any    		=None
																												with xopen(UpperCAmelCase__		,					'''rb'''		,					use_auth_token=UpperCAmelCase__   ) as f:
																																		lowercase :							int    		=BytesIO(f.read()   )
																												lowercase :							int    		=PIL.Image.open(bytes_   )
										else:
																lowercase :							List[Any]    		=PIL.Image.open(BytesIO(bytes_   )   )
										image.load()  # to avoid "Too many open files" errors
										return image







				def  lowerCamelCase_	(       self     :		Optional[Any]   ):
										'''simple docstring'''
										from .features import Value

										return (
										    self
										    if self.decode
										    else {
										        "bytes": Value('''binary'''   ),
										        "path": Value('''string'''   ),
										    }
										)







				def  lowerCamelCase_	(       self     :		Tuple		,					UpperCAmelCase__     :		Union[pa.StringArray, pa.StructArray, pa.ListArray]   ):
										'''simple docstring'''
										if pa.types.is_string(storage.type   ):
																lowercase :							str    		=pa.array([None] * len(UpperCAmelCase__   )		,					type=pa.binary()   )
																lowercase :							str    		=pa.StructArray.from_arrays([bytes_array, storage]		,					['''bytes''', '''path''']		,					mask=storage.is_null()   )
										elif pa.types.is_binary(storage.type   ):
																lowercase :							int    		=pa.array([None] * len(UpperCAmelCase__   )		,					type=pa.string()   )
																lowercase :							Union[str, Any]    		=pa.StructArray.from_arrays([storage, path_array]		,					['''bytes''', '''path''']		,					mask=storage.is_null()   )
										elif pa.types.is_struct(storage.type   ):
																if storage.type.get_field_index('''bytes'''   ) >= 0:
																						lowercase :							Union[str, Any]    		=storage.field('''bytes'''   )
																else:
																						lowercase :							Dict    		=pa.array([None] * len(UpperCAmelCase__   )		,					type=pa.binary()   )
																if storage.type.get_field_index('''path'''   ) >= 0:
																						lowercase :							Union[str, Any]    		=storage.field('''path'''   )
																else:
																						lowercase :							List[str]    		=pa.array([None] * len(UpperCAmelCase__   )		,					type=pa.string()   )
																lowercase :							Optional[Any]    		=pa.StructArray.from_arrays([bytes_array, path_array]		,					['''bytes''', '''path''']		,					mask=storage.is_null()   )
										elif pa.types.is_list(storage.type   ):
																lowercase :							Optional[Any]    		=pa.array(
																    [encode_np_array(np.array(UpperCAmelCase__   )   )['''bytes'''] if arr is not None else None for arr in storage.to_pylist()]		,					type=pa.binary()		,					)
																lowercase :							int    		=pa.array([None] * len(UpperCAmelCase__   )		,					type=pa.string()   )
																lowercase :							List[str]    		=pa.StructArray.from_arrays(
																    [bytes_array, path_array]		,					['''bytes''', '''path''']		,					mask=bytes_array.is_null()   )
										return array_cast(UpperCAmelCase__		,					self.pa_type   )







				def  lowerCamelCase_	(       self     :		Optional[int]		,					UpperCAmelCase__     :		pa.StructArray   ):
										'''simple docstring'''

										@no_op_if_value_is_null
										def path_to_bytes(UpperCAmelCase__     :		Optional[int]   ):
																with xopen(UpperCAmelCase__		,					'''rb'''   ) as f:
																						lowercase :							Union[str, Any]    		=f.read()
																return bytes_

										lowercase :							Optional[Any]    		=pa.array(
										    [
										        (path_to_bytes(x['''path''']   ) if x['''bytes'''] is None else x['''bytes''']) if x is not None else None
										        for x in storage.to_pylist()
										    ]		,					type=pa.binary()		,					)
										lowercase :							List[Any]    		=pa.array(
										    [os.path.basename(UpperCAmelCase__   ) if path is not None else None for path in storage.field('''path'''   ).to_pylist()]		,					type=pa.string()		,					)
										lowercase :							str    		=pa.StructArray.from_arrays([bytes_array, path_array]		,					['''bytes''', '''path''']		,					mask=bytes_array.is_null()   )
										return array_cast(UpperCAmelCase__		,					self.pa_type   )






def 				_lowerCAmelCase			(   )				->  List[str]:
						if config.PIL_AVAILABLE:
												import PIL.Image
						else:
												raise ImportError('''To support encoding images, please install \'Pillow\'.'''						)

						global _IMAGE_COMPRESSION_FORMATS
						if _IMAGE_COMPRESSION_FORMATS is None:
												PIL.Image.init()
												lowercase :							List[Any]    		=list(set(PIL.Image.OPEN.keys()						) & set(PIL.Image.SAVE.keys()						)						)
						return _IMAGE_COMPRESSION_FORMATS






def 				_lowerCAmelCase			(   __magic_name__						:     "PIL.Image.Image"						)				->  bytes:
						lowercase :							int    		=BytesIO()
						if image.format in list_image_compression_formats():
												lowercase :							Any    		=image.format
						else:
												lowercase :							Optional[Any]    		='''PNG''' if image.mode in ['''1''', '''L''', '''LA''', '''RGB''', '''RGBA'''] else '''TIFF'''
						image.save(__magic_name__   , format=__magic_name__						)
						return buffer.getvalue()






def 				_lowerCAmelCase			(   __magic_name__						:     "PIL.Image.Image"						)				->  dict:
						if hasattr(__magic_name__   , '''filename'''						) and image.filename != "":
												return {"path": image.filename, "bytes": None}
						else:
												return {"path": None, "bytes": image_to_bytes(__magic_name__						)}






def 				_lowerCAmelCase			(   __magic_name__						:     np.ndarray						)				->  dict:
						if config.PIL_AVAILABLE:
												import PIL.Image
						else:
												raise ImportError('''To support encoding images, please install \'Pillow\'.'''						)

						lowercase :							Optional[int]    		=array.dtype
						lowercase :							Dict    		=dtype.byteorder if dtype.byteorder != '''=''' else _NATIVE_BYTEORDER
						lowercase :							Dict    		=dtype.kind
						lowercase :							int    		=dtype.itemsize

						lowercase :							int    		=None

						# Multi-channel array case (only np.dtype("|u1") is allowed)
						if array.shape[2:]:
												lowercase :							Any    		=np.dtype('''|u1'''						)
												if dtype_kind not in ["u", "i"]:
																		raise TypeError(
																		    f'''Unsupported array dtype {dtype} for image encoding. Only {dest_dtype} is supported for multi-channel arrays.'''						)
												if dtype is not dest_dtype:
																		warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''						)
    # Exact match
						elif dtype in _VALID_IMAGE_ARRAY_DTPYES:
												lowercase :							str    		=dtype
						else:  # Downcast the type within the kind (np.can_cast(from_type, to_type, casting="same_kind") doesn't behave as expected, so do it manually)
												while dtype_itemsize >= 1:
																		lowercase :							Optional[Any]    		=dtype_byteorder + dtype_kind + str(__magic_name__						)
																		lowercase :							Union[str, Any]    		=np.dtype(__magic_name__						)
																		if dest_dtype in _VALID_IMAGE_ARRAY_DTPYES:
																								warnings.warn(f'''Downcasting array dtype {dtype} to {dest_dtype} to be compatible with \'Pillow\''''						)
																								break
																		else:
																								dtype_itemsize //= 2

						if dest_dtype is None:
												raise TypeError(
												    f'''Cannot convert dtype {dtype} to a valid image dtype. Valid image dtypes: {_VALID_IMAGE_ARRAY_DTPYES}'''						)
						lowercase :							Dict    		=PIL.Image.fromarray(array.astype(__magic_name__						)						)
						return {"path": None, "bytes": image_to_bytes(__magic_name__						)}







def 				_lowerCAmelCase			(   __magic_name__						:     Union[List[str], List[dict], List[np.ndarray], List["PIL.Image.Image"]]						)				->  List[dict]:
						if config.PIL_AVAILABLE:
												import PIL.Image
						else:
												raise ImportError('''To support encoding images, please install \'Pillow\'.'''						)

						if objs:
												lowercase		,	lowercase :							Tuple    		=first_non_null_value(__magic_name__						)
												if isinstance(__magic_name__   , __magic_name__						):
																		return [{"path": obj, "bytes": None} if obj is not None else None for obj in objs]
												if isinstance(__magic_name__   , np.ndarray						):
																		lowercase :							Optional[int]    		=no_op_if_value_is_null(__magic_name__						)
																		return [obj_to_image_dict_func(__magic_name__						) for obj in objs]
												elif isinstance(__magic_name__   , PIL.Image.Image						):
																		lowercase :							Optional[int]    		=no_op_if_value_is_null(__magic_name__						)
																		return [obj_to_image_dict_func(__magic_name__						) for obj in objs]
												else:
																		return objs
						else:
												return objs






"""simple docstring"""



import os

import pytest
from attr import dataclass


SCREAMING_SNAKE_CASE__:List[str]									=			"""us-east-1"""  # defaults region






@dataclass
class  snake_case__      :
 _snake_case       :					str
 _snake_case       :					Optional[Any]							  =						"""arn:aws:iam::558105141721:role/sagemaker_execution_role"""
 _snake_case       :					Optional[Any]							  =						{
     """task_name""": """mnli""",
     """per_device_train_batch_size""": 16,
     """per_device_eval_batch_size""": 16,
     """do_train""": True,
     """do_eval""": True,
     """do_predict""": True,
     """output_dir""": """/opt/ml/model""",
     """overwrite_output_dir""": True,
     """max_steps""": 500,
     """save_steps""": 5_500,
 }
 _snake_case       :					List[str]							  =						{**hyperparameters, """max_steps""": 1_000}


 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"eval_accuracy.*=\D*(.*?)$"},
                   {"Name": "eval_loss", "Regex": r"eval_loss.*=\D*(.*?)$"},
               ]
        else:
               return [
                   {"Name": "train_runtime", "Regex": r"train_runtime.*=\D*(.*?)$"},
                   {"Name": "eval_accuracy", "Regex": r"loss.*=\D*(.*?)]?$"},
                   {"Name": "eval_loss", "Regex": r"sparse_categorical_accuracy.*=\D*(.*?)]?$"},
               ]


 @property
 def 						a__   ( self						):
        return F"{self.framework}-transfromers-test"


 @property
 def 						a__   ( self						):
        return F"./tests/sagemaker/scripts/{self.framework}"






 @property
 def 						a__   ( self						):
        if self.framework == "pytorch":
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-pytorch-training:1.7.1-transformers4.6.1-gpu-py36-cu110-ubuntu18.04"
        else:
               return "763104351884.dkr.ecr.us-east-1.amazonaws.com/huggingface-tensorflow-training:2.4.1-transformers4.6.1-gpu-py37-cu110-ubuntu18.04"



@pytest.fixture(scope="class"				)
def 	_lowerCamelCase(	a				):
       __a       =    SageMakerTestEnvironment(framework=request.cls.framework				)
